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ESP32-based Multi-User Secret Key Generation System

Dongyang Li, Linning Peng, Hua Fu, Fengsheng Wang, Ye Tian, He Shen

Published Jul 8, 2023• 2 citations
Publisher

Abstract

This paper proposes a multi-user secret key generation system using off-the-shelf ESP32 modules. We first propose a new differential quantization algorithm and contrast the features of differential quantization with uniform quantization and existing equal-probability quantization. Then, we propose a differential compensation algorithm, which can effectively improve the randomness of differential quantization. The improved randomness is higher than that of equal-probability quantization, and the key disagreement rate (KDR) is lower than that of equal-probability quantization. After that, we create an ESP32-based multi-user secret key generation system. We compare the differences of KDR in three movement modes, stable, people-move and trolly-move, and in two scenarios, line-of-sight (LOS) and non-line-of-sight (NLOS). The results show that the KDR with third-order quantization is less than 0.1 for the LOS scene and less than 0.15 for the NLOS scene.

DOI
10.1109/icsip57908.2023.10270955

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This paper proposes a multi-user secret key generation system using off-the-shelf ESP32 modules. We first propose a new differential quantization algorithm and contrast the features of differential quantization with uniform quantization and existing equal-probability quantization. Then, we propose a differential compensation algorithm, which can effectively improve the randomness of differential quantization. The improved randomness is higher than that of equal-probability quantization, and the key disagreement rate (KDR) is lower than that of equal-probability quantization. After that, we create an ESP32-based multi-user secret key generation system. We compare the differences of KDR in three movement modes, stable, people-move and trolly-move, and in two scenarios, line-of-sight (LOS) and non-line-of-sight (NLOS). The results show that the KDR with third-order quantization is less than 0.1 for the LOS scene and less than 0.15 for the NLOS scene.
The Secret Key Generation (SKG) scheme that exploits the reciprocity and uniqueness of wireless channel between two users plays a significant part in a new increasing distributed security system. The scheme performance can be distinguished based on the low value of Key disagreement Rate (KDR), the high value of Key Generation Rate (KGR), as well as the fulfillment of the NIST randomness standard. The previous SKG scheme has a high KDR due to a direct quantization of a measurement result of the Received Signal Strength (RSS). To overcome the above issue, we conduct a pre-processing of measurement result before quantization with the Kalman method. The pre-process is carried out to improve the channel reciprocity between two legitimate users with the objective to reduce the bit mismatch. Through an experiment, we propose a new quantization scheme called a Modified Multi-Bit (MMB) that uses a multi-bit system on every level of quantization. The test results show that the proposed combination of preprocessing and the MMB scheme has a better performance compared to the existing schemes in terms of KDR and KGR. The Secret Key generated by our scheme also fulfills the NIST randomness standard.
Wireless key generation systems often face challenges in achieving low Key Disagreement Rates (KDR) and Key Generation Rate (KGR) across diverse scenarios, limiting their reliability and efficiency. This paper presents a wireless key generation system based on the ESP32 platform, combining quantization methods and error correction codes to address these issues through comparative experiments. To obtain the ideal quantization order in different scenarios, we propose an adaptive quantization order selection model based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results indicate that the performance of quantization methods is influenced by both the specific scenarios and the quantization order employed. Moreover, the combination of Joint Amplitude-Phase Equiprobable Quantization (JAPEQ) and Low-Density Parity-Check Codes (LDPC) outperforms other combinations in terms of KDR. Finally, our proposed adaptive quantization order selection model demonstrates ideal performance in terms of both key consistency and generation rate.
This paper explores secret key generation in 5G and beyond LiFi networks using visible light in the downlink and infrared in the uplink. Unlike the existing works, we focus on a realistic indoor environment with multi-user mobility. Given inaccuracies in high-frequency channel models, we introduce the first deep learning model that combines the channel probing and quantization phases to generate initial secret keys with a minimal key disagreement rate (KDR) of 16% between the uplink and downlink, leading to a key generation rate (KGR) of 79 bits/s after information reconciliation. We show that LiFi channel statistics suffer from concept drifts with user density changes in the room. This increases the KDR by 28% - 44% and the generated keys fail to pass the NIST randomness tests. As a countermeasure, we introduce a voting ensemble model that mitigates concept drifts, maintaining a stable 16% KDR, 79 bits/s KGR, and passing NIST tests, despite the varying user densities.
Channel state information (CSI)-based channel key generation technology has been widely studied in recent years, but it lacks the application in multi-user access scenarios, and the commonly used CSI extraction tools are not easy to generalize to Internet of Things (IoT) scenarios. The emerging ESP CSI tool overcomes the limitations of traditional tools, but no one has used it for key generation related research. We transformed ESP CSI tool, proposed a numbered CSI extraction tool for key generation in multi-user access scenarios. Then, we conceived a corresponding key generation scheme based on this tool. Experimental results show that CSI data with high reciprocity can be obtained in different scenarios, and can generate keys with low key disagreement rate (KDR), high randomness, and anti-eavesdropping.
Traditional wireless security schemes neglect the unique properties of the wireless channel. Exploiting physical layer can significantly enhance the conventional security schemes. However, the robustness and bit mismatch rate of current physical-layer-based schemes still need to be improved. Conventional two thresholds quantization is not stable when inserting or removing intermediate objects between legitimate users, and the consistency of the generated secret key is low due to small fluctuations. In this article, we propose a differential secret key generation scheme based on weighted exponential moving average (WEMA) method. Differential quantization is utilized to enhance the robustness of the secret key generation scheme. Furthermore, we prove that WEMA method can effectively smooth out short-term fluctuations and decreasing the bit mismatch rate. Finally, we present a numerical simulation to demonstrate the feasibility of our proposal.
Secure communication in Internet of Things (IoT) devices can be improved through physical-layer secret key generation. In secret key generation, an information reconciliation process is required to match the bit sequences between the two communicating devices. However, this process often involves complex statistical computations that increase system overhead and require the transmission of bits over an insecure public channel. Therefore, this research presents a lightweight reconciliation algorithm based on minimal bit syndrome to reduce overhead and improve key security. The proposed method processes quantized Received Signal Strength (RSS) values into 8-bit block syndrome-based reconciliation mechanism and only the 8-bit syndromes from mismatched block are transmitted over the public channel. System testing was conducted under both static and dynamic scenarios in an indoor area using Raspberry Pi nodes. The results show that the proposed scheme demonstrates an improvement in the Key Generation Rate (KGR) by 2.2% and reduces the processing time by 11.6% in dynamic scenario and 8.3% in the static scenario. Also, the proposed scheme reduce the quantization and reconciliation time by 50% compared to previous research. Furthermore, the proposed method maintained a Key Disagreement Rate (KDR) of 0% across all block size and successfully passed the NIST randomness and complexity test, with all test parameters achieving p-values above 0.01. These result indicate that the proposed scheme improve the performance of existing secret key generation methods and it is suitable for implementation in IoT devices.